Explained: 7 Key Attributes of Data for creating a strong Data Strategy

In today's data-driven world, organisations recognise the immense value that data holds for driving insights and making informed decisions.

However, to harness the full potential of data, it is essential to develop a robust data strategy that takes into account its unique attributes.

In this article, we will delve into seven key attributes of data that contribute to creating a strong data strategy, exploring each attribute's significance and providing real-world examples to illustrate their importance.

1. Data travels fast

Data in the digital age moves at an unprecedented speed.

With the proliferation of the internet, social media, and interconnected devices, information spreads rapidly across various platforms.

As a result, organisations must adapt to this fast-paced environment by implementing real-time data processing and analytics capabilities.

For example, in the financial industry, high-frequency trading algorithms rely on real-time market data to make split-second investment decisions and capitalise on market opportunities.

2. Data never changes by itself

Data is inherently static and does not change autonomously. It remains the same until acted upon by an external force.

This attribute emphasises the importance of data governance and the need to ensure data accuracy, consistency, and integrity throughout its lifecycle.

In healthcare, accurate patient records are vital for delivering quality care.

By implementing data governance practices, healthcare providers can ensure that patient information remains up-to-date, preventing errors and enabling informed medical decisions.

3. Data does not yield value on its own

While data holds tremendous potential, it is important to recognise that simply storing data does not automatically produce insights or create value.

Organisations need to focus on data analysis, employing advanced analytics techniques such as machine learning, predictive modelling, and data visualisation to extract meaningful insights.

For instance, e-commerce companies analyse customer browsing and purchase history to personalise product recommendations, leading to improved customer experiences and increased sales.

4. Data classification exercise

To manage data effectively, organisations must classify and categorise it based on its sensitivity, importance, and regulatory requirements.

This attribute highlights the significance of data classification exercises, where data is assessed to determine appropriate security measures, access controls, and retention policies.

Governments, for example, classify data into various levels of sensitivity, such as confidential, secret, or top-secret, to ensure appropriate handling and protection of classified information.

5. Data has gravity

Data possesses a gravitational force that attracts other data, applications, and processes.

This attribute underscores the interconnected nature of data, where it often serves as a foundation for building advanced analytics systems or supporting integration between various business applications.

Consider a customer relationship management (CRM) system, where customer data serves as the core foundation, pulling together sales, marketing, and customer support functions into a unified platform for a holistic view of customer interactions.

6. Data duplication for latency and throughput

Despite efforts to maintain a single source of truth, duplicating data can be necessary to meet latency, throughput, and accessibility requirements.

In distributed systems, duplicating data across different locations ensures faster access, reduces network latency, and enables fault tolerance.

For instance, content delivery networks (CDNs) duplicate and distribute website data to multiple servers worldwide, allowing users to access content from the server closest to them, resulting in faster load times and improved user experience.

7. Speed of data processing governed by architectural constructs

The speed at which data can be processed is influenced by the underlying architectural constructs employed.

Factors such as storage systems, data processing frameworks, and network infrastructure impact data processing capabilities.

For example, big data processing frameworks like Apache Hadoop and Apache Spark leverage distributed computing to process massive volumes of data in parallel, enabling organisations to gain insights from vast datasets efficiently.

In conclusion, developing a strong data strategy requires a deep understanding of the unique attributes of data.

By recognising that data travels fast, does not change autonomously, and does not yield value on its own, organisations can focus on deriving insights and generating value through advanced analytics.

Implementing data classification exercises, acknowledging data gravity, and understanding the need for data duplication and efficient data processing architectures are vital components of an effective data strategy.

By leveraging these attributes, organisations can unlock the true power of data and drive success in today's data-centric landscape.